Embedded analytics of animal images
Due to the large increase of image data in animal surveillance, an effective and efficient way of labeling said data is required. Over the past few years the Climate-ecological Observatory for Arctic Tundra (COAT) project have deployed dozens of cameras in eastern Finnmark, Norway during winter, whi...
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UiT Norges arktiske universitet
2017
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Online Access: | https://hdl.handle.net/10037/12000 |
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ftunivtroemsoe:oai:munin.uit.no:10037/12000 2023-05-15T15:18:01+02:00 Embedded analytics of animal images Thomassen, Sigurd 2017-12-14 https://hdl.handle.net/10037/12000 eng eng UiT Norges arktiske universitet UiT The Arctic University of Norway https://hdl.handle.net/10037/12000 openAccess Copyright 2017 The Author(s) VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kommunikasjon og distribuerte systemer: 423 VDP::Mathematics and natural science: 400::Information and communication science: 420::Communication and distributed systems: 423 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kunnskapsbaserte systemer: 425 VDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Systemutvikling og – arbeid: 426 VDP::Mathematics and natural science: 400::Information and communication science: 420::System development and system design: 426 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering visualisering signalbehandling bildeanalyse: 429 VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 INF-3981 Master thesis Mastergradsoppgave 2017 ftunivtroemsoe 2021-06-25T17:55:41Z Due to the large increase of image data in animal surveillance, an effective and efficient way of labeling said data is required. Over the past few years the Climate-ecological Observatory for Arctic Tundra (COAT) project have deployed dozens of cameras in eastern Finnmark, Norway during winter, which have resulted in a large volume of wildlife images which is used to document the effects of climate change on animal ecosystems in the area. The images are manually labeled by biologists, and is a time-consuming task. This thesis presents the architecture, design and implementation of an image classification system to be used with the camera traps for in-situ analytics on accumulated image data for periodical updates. The system will automatically classify and label the images taken by the cameras. Using state-of-the-art Convolutional Neural Networks (CNNs) we train the system on previously labeled COAT image data. We train four different models based on the MobileNet architecture. The models vary in number of weights, and input image resolution. Results show that we can automatically classify images on a small computer like the Raspberry Pi, with an accuracy of 81.1% at 1.17 FPS, and a model size of 17Mb. In comparison a GPU computer achieves the same accuracy and model size, but it has a classification speed of 12.5 FPS. Master Thesis Arctic Climate change Finnmark Tundra Finnmark University of Tromsø: Munin Open Research Archive Arctic Norway |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kommunikasjon og distribuerte systemer: 423 VDP::Mathematics and natural science: 400::Information and communication science: 420::Communication and distributed systems: 423 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kunnskapsbaserte systemer: 425 VDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Systemutvikling og – arbeid: 426 VDP::Mathematics and natural science: 400::Information and communication science: 420::System development and system design: 426 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering visualisering signalbehandling bildeanalyse: 429 VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 INF-3981 |
spellingShingle |
VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kommunikasjon og distribuerte systemer: 423 VDP::Mathematics and natural science: 400::Information and communication science: 420::Communication and distributed systems: 423 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kunnskapsbaserte systemer: 425 VDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Systemutvikling og – arbeid: 426 VDP::Mathematics and natural science: 400::Information and communication science: 420::System development and system design: 426 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering visualisering signalbehandling bildeanalyse: 429 VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 INF-3981 Thomassen, Sigurd Embedded analytics of animal images |
topic_facet |
VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kommunikasjon og distribuerte systemer: 423 VDP::Mathematics and natural science: 400::Information and communication science: 420::Communication and distributed systems: 423 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Kunnskapsbaserte systemer: 425 VDP::Mathematics and natural science: 400::Information and communication science: 420::Knowledge based systems: 425 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Systemutvikling og – arbeid: 426 VDP::Mathematics and natural science: 400::Information and communication science: 420::System development and system design: 426 VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering visualisering signalbehandling bildeanalyse: 429 VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation visualization signal processing image processing: 429 INF-3981 |
description |
Due to the large increase of image data in animal surveillance, an effective and efficient way of labeling said data is required. Over the past few years the Climate-ecological Observatory for Arctic Tundra (COAT) project have deployed dozens of cameras in eastern Finnmark, Norway during winter, which have resulted in a large volume of wildlife images which is used to document the effects of climate change on animal ecosystems in the area. The images are manually labeled by biologists, and is a time-consuming task. This thesis presents the architecture, design and implementation of an image classification system to be used with the camera traps for in-situ analytics on accumulated image data for periodical updates. The system will automatically classify and label the images taken by the cameras. Using state-of-the-art Convolutional Neural Networks (CNNs) we train the system on previously labeled COAT image data. We train four different models based on the MobileNet architecture. The models vary in number of weights, and input image resolution. Results show that we can automatically classify images on a small computer like the Raspberry Pi, with an accuracy of 81.1% at 1.17 FPS, and a model size of 17Mb. In comparison a GPU computer achieves the same accuracy and model size, but it has a classification speed of 12.5 FPS. |
format |
Master Thesis |
author |
Thomassen, Sigurd |
author_facet |
Thomassen, Sigurd |
author_sort |
Thomassen, Sigurd |
title |
Embedded analytics of animal images |
title_short |
Embedded analytics of animal images |
title_full |
Embedded analytics of animal images |
title_fullStr |
Embedded analytics of animal images |
title_full_unstemmed |
Embedded analytics of animal images |
title_sort |
embedded analytics of animal images |
publisher |
UiT Norges arktiske universitet |
publishDate |
2017 |
url |
https://hdl.handle.net/10037/12000 |
geographic |
Arctic Norway |
geographic_facet |
Arctic Norway |
genre |
Arctic Climate change Finnmark Tundra Finnmark |
genre_facet |
Arctic Climate change Finnmark Tundra Finnmark |
op_relation |
https://hdl.handle.net/10037/12000 |
op_rights |
openAccess Copyright 2017 The Author(s) |
_version_ |
1766348249974702080 |